IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
IOSR Journal of Mechanical and Civil Engineering (IOSR-JMCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of mechanical and civil engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in mechanical and civil engineering. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
This slide proposed a method to authenticate a signature in off-line. Our proposed method uses "Harris Corner Detector", "Orientation Assignment" , "KNN Classifier", "Hungarian Algorithm".
Authentication of a person is the major concern in this era for security purposes. In biometric systems Signature is one of the behavioural features used for the authentication purpose. In this paper we work on the offline signature collected through different persons. Morphological operations are applied on these signature images with Hough transform to determine regular shape which assists in authentication process. The values extracted from this Hough space is used in the feed forward neural network which is trained using back-propagation algorithm. After the different training stages efficiency found above more than 95%. Application of this system will be in the security concerned fields, in the defence security, biometric authentication, as biometric computer protection or as method of the analysis of person’s behaviour changes.
An offline signature recognition and verification system based on neural networkeSAT Journals
Abstract Various techniques are already introduced for personal identification and verification based on different types of biometrics which can be physiological or behavioral. Signatures lies in the category of behavioral biometric which can distort or changed with course of time. Signatures are considered to be most promising authentication method in all legal and financial documents. It is necessary to verify signers and their respective signatures. This paper presents an Offline Signature recognition and verification system(SRVS). In this system signature database of signature images is created, followed by image preprocessing, feature extraction, neural network design and training, and classification of signature as genuine or counterfeit. Keywords: biometrics, neural network design, feature extraction, classification etc.
Handwritten Signature Verification using Artificial Neural NetworkEditor IJMTER
This paper reviews various Signature Verification approaches; various feature sets,
various online databases and types of features. Processing on an online database, post extracting a
combination of global and local features onto a signature as an image, using MultiLayer Perceptron Feed
Forward Network alongwith Back Propogation Algorithm for training is proposed to classify a genuine
and forged (random, simple and skilled) offline signatures.
IOSR Journal of Mechanical and Civil Engineering (IOSR-JMCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of mechanical and civil engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in mechanical and civil engineering. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
This slide proposed a method to authenticate a signature in off-line. Our proposed method uses "Harris Corner Detector", "Orientation Assignment" , "KNN Classifier", "Hungarian Algorithm".
Authentication of a person is the major concern in this era for security purposes. In biometric systems Signature is one of the behavioural features used for the authentication purpose. In this paper we work on the offline signature collected through different persons. Morphological operations are applied on these signature images with Hough transform to determine regular shape which assists in authentication process. The values extracted from this Hough space is used in the feed forward neural network which is trained using back-propagation algorithm. After the different training stages efficiency found above more than 95%. Application of this system will be in the security concerned fields, in the defence security, biometric authentication, as biometric computer protection or as method of the analysis of person’s behaviour changes.
An offline signature recognition and verification system based on neural networkeSAT Journals
Abstract Various techniques are already introduced for personal identification and verification based on different types of biometrics which can be physiological or behavioral. Signatures lies in the category of behavioral biometric which can distort or changed with course of time. Signatures are considered to be most promising authentication method in all legal and financial documents. It is necessary to verify signers and their respective signatures. This paper presents an Offline Signature recognition and verification system(SRVS). In this system signature database of signature images is created, followed by image preprocessing, feature extraction, neural network design and training, and classification of signature as genuine or counterfeit. Keywords: biometrics, neural network design, feature extraction, classification etc.
Handwritten Signature Verification using Artificial Neural NetworkEditor IJMTER
This paper reviews various Signature Verification approaches; various feature sets,
various online databases and types of features. Processing on an online database, post extracting a
combination of global and local features onto a signature as an image, using MultiLayer Perceptron Feed
Forward Network alongwith Back Propogation Algorithm for training is proposed to classify a genuine
and forged (random, simple and skilled) offline signatures.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Offline handwritten signature identification using adaptive window positionin...sipij
The paper presents to address this challenge, we have proposed the use of Adaptive Window Positioning
technique which focuses on not just the meaning of the handwritten signature but also on the individuality
of the writer. This innovative technique divides the handwritten signature into 13 small windows of size nxn
(13x13). This size should be large enough to contain ample information about the style of the author and
small enough to ensure a good identification performance. The process was tested with a GPDS dataset
containing 4870 signature samples from 90 different writers by comparing the robust features of the test
signature with that of the user’s signature using an appropriate classifier. Experimental results reveal that
adaptive window positioning technique proved to be the efficient and reliable method for accurate
signature feature extraction for the identification of offline handwritten signatures .The contribution of this
technique can be used to detect signatures signed under emotional duress
OFFLINE SIGNATURE VERIFICATION SYSTEM FOR BANK CHEQUES USING ZERNIKE MOMENTS,...ijaia
Handwritten signature is the most accepted and economical means of personnel authentication. It can be
verified using online or offline schemes. This paper proposes a signature verification model by combining
Zernike moments feature with circularity and aspect ratio. Unlike characters, signatures vary each time
because of its behavioural biometric property. Signatures can be identified based on their shape. Moments
are the good translational and scale invariant shape descriptors. The amplitude and the phase of Zernike
moments, circularity and aspect ratio of the signature are the features that are extracted and combined for
the verification purpose and are fed to the Feedforward Backpropagation Neural Network. This Neural
Network classifies the signature into genuine or forged. Experimental results reveal that this methodology
of combining zernike moments along with the two mentioned geometrical properties give higher accuracy
than using them individually. The combination of these feature vector yields a mean accuracy of 95.83%.
When this approach is compared with the literature, it proves to be more effective.
ARABIC ONLINE HANDWRITING RECOGNITION USING NEURAL NETWORKijaia
This article presents the development of an Arabic online handwriting recognition system. To develop our
system, we have chosen the neural network approach. It offers solutions for most of the difficulties linked
to Arabic script recognition. We test the approach with our collected databases. This system shows a good
result and it has a high accuracy (98.50% for characters, 96.90% for words).
Fraud Detection Using Signature RecognitionTejraj Thakor
The signature of person is an important bio metric of a human being which can be used to authenticate human identity. The problem arises when someone decide to imitate our signature and steal our identity.
The Image of human signature is collected by camera of mobile phone which can extract dynamic and spatial information of the signature based on Image processing techniques like Convert to gray scale, Noise Removal, Normalization, Border Elimination and Feature Extraction techniques.
The signature matching is depending on SVM. The SVM classifier is trained with sample images in database obtained from those individuals whose signatures have to be authenticated by the system. In our proposed system SQLite database as a back-end and Android platform as a front-end.
Offline Signature Verification Using Local Radon Transform and Support Vector...CSCJournals
In this paper, we propose a new method for signature verification using local Radon Transform. The proposed method uses Radon Transform locally as feature extractor and Support Vector Machine (SVM) as classifier. The main idea of our method is using Radon Transform locally for line segments detection and feature extraction, against using it globally. The advantages of the proposed method are robustness to noise, size invariance and shift invariance. Having used a dataset of 600 signatures from 20 Persian writers, and another dataset of 924 signatures from 22 English writers, our system achieves good results. The experimental results of our method are compared with two other methods. This comparison shows that our method has good performance for signature identification and verification in different cultures.
An offline signature verification using pixels intensity levelsSalam Shah
Offline signature recognition has great importance in our day to day activities. Researchers are trying to use them as biometric identification in various areas like banks, security systems and for other identification purposes. Fingerprints, iris, thumb impression and face detection based biometrics are successfully used for identification of individuals because of their static nature. However, people’s signatures show variability that makes it difficult to recognize the original signatures correctly and to use them as biometrics. The handwritten signatures have importance in banks for cheque, credit card processing, legal and financial transactions, and the signatures are the main target of fraudulence. To deal with complex signatures, there should be a robust signature verification method in places such as banks that can correctly classify the signatures into genuine or forgery to avoid financial frauds. This paper, presents a pixels intensity level based offline signature verification model for the correct classification of signatures. To achieve the target, three statistical classifiers; Decision Tree (J48), probability based Naïve Bayes (NB tree) and Euclidean distance based k-Nearest Neighbor (IBk), are used.
For comparison of the accuracy rates of offline signatures with online signatures, three classifiers were applied on online signature database and achieved a 99.90% accuracy rate with decision tree (J48), 99.82% with Naïve Bayes Tree and 98.11% with K-Nearest Neighbor (with 10 fold cross validation). The results of offline signatures were 64.97% accuracy rate with decision tree (J48), 76.16% with Naïve Bayes Tree and 91.91% with k-Nearest Neighbor (IBk) (without forgeries). The accuracy rate dropped with the inclusion of forgery signatures as, 55.63% accuracy rate with decision tree (J48), 67.02% with Naïve Bayes Tree and 88.12% (with forgeries).
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Proposed Method for Off-line Signature Recognition and Verification using Neu...Editor IJMTER
Computers have become common and are used in almost every field including financial
transactions, thus providing additional security measures is necessary. According to consumer’s
expectations, these security measures must be cheap, reliable and un-intrusive to the authorized
person. The technique which meets these requirements is handwritten signature verification.
Signature verification technique has advantage over other biometric techniques: including voice, iris,
fingerprint, palm etc. as it is mostly used for daily routine procedures link banking operations,
document analysis, electronic funds transfer, access control and many other. Most importantly, it’s
easy and people are less likely to object it. Proposed technique involves using a new approach that
depends on a neural network which enables the user to recognize whether a signature is original or a
fraud. Scanned images are introduced into the computer, their quality is modified with the help of
image enhancement and noise reduction techniques, specific features are extracted and neural
network is trained, The different stages of the process includes: image pre-processing, feature
extraction and pattern recognition through neural networks.
Text content dependent writer identificationeSAT Journals
Abstract
Text content based personal Identification system is vital in resolving problem of identifying unknown document’s writer using a
set of handwritten samples from alleged known writers. Text written on paper document is usually captured as image by scanner
or camera for computer processing. The most challenging problem encounter in text image processing is extraction of robust
feature vector from a set of inconstant handwritten text images obtained from the same writer at different time. In this work new
feature extraction method is engaged to produce active text features for developing an effective personal identification system.
The feature formed feature vector which is fed as input data into classification algorithm based on Support Vector Machine
(SVM). Experiment was conducted to identify writers of query handwritten texts. Result show satisfactory performance of the
proposed system, it was able to identify writers of query handwritten texts.
Keywords: Handwritten Text, Feature Vector, Identification and Support Vector Machine.
Offline Handwritten Signature Identification and Verification using Multi-Res...CSCJournals
In this paper, we are proposing a new method for offline (static) handwritten signature identification and verification based on Gabor wavelet transform. The whole idea is offering a simple and robust method for extracting features based on Gabor Wavelet which the dependency of the method to the nationality of signer has been reduced to its minimal. After pre-processing stage that contains noise reduction and signature image normalisation by size and rotation, a virtual grid is placed on the signature image. Gabor wavelet coefficients with different frequencies and directions are computed on each points of this grid and then fed into a classifier. The shortest weighted distance has been used as the classifier. The weight that is used as the coefficient for computing the shortest distance is based on the distribution of instances in each of signature classes. As it was pointed out earlier, one of the advantages of this system is its capability of signature identification and verification of different nationalities; thus it has been tested on four signature dataset with different nationalities including Iranian, Turkish, South African and Spanish signatures. Experimental results and the comparison of the proposed system with other systems are consistent with desirable outcomes. Despite the use of the simplest method of classification i.e. the nearest neighbour, the proposed algorithm in comparison with other algorithms has very good capabilities. Comparing the results of our system with the accuracy of human\'s identification and verification, it shows that human identification is more accurate but our proposed system has a lower error rate in verification.
OFFLINE SIGNATURE RECOGNITION VIA CONVOLUTIONAL NEURAL NETWORK AND MULTIPLE C...IJNSA Journal
One of the most important processes used by companies to safeguard the security of information and prevent it from unauthorized access or penetration is the signature process. As businesses and individuals move into the digital age, a computerized system that can discern between genuine and faked signatures is crucial for protecting people's authorization and determining what permissions they have. In this paper, we used Pre-Trained CNN for extracts features from genuine and forged signatures, and three widely used classification algorithms, SVM (Support Vector Machine), NB (Naive Bayes) and KNN (k-nearest neighbors), these algorithms are compared to calculate the run time, classification error, classification loss, and accuracy for test-set consist of signature images (genuine and forgery). Three classifiers have been applied using (UTSig) dataset; where run time, classification error, classification loss and accuracy were calculated for each classifier in the verification phase, the results showed that the SVM and KNN got the best accuracy (76.21), while the SVM got the best run time (0.13) result among other classifiers, therefore the SVM classifier got the best result among the other classifiers in terms of our measures.
Freeman Chain Code (FCC) Representation in Signature Fraud Detection Based On...CSCJournals
This paper presents a signature verification system that used Freeman Chain Code (FCC) as directional feature and data representation. There are 47 features were extracted from the signature images from six global features. Before extracting the features, the raw images were undergoing pre-processing stages which were binarization, noise removal by using media filter, cropping and thinning to produce Thinned Binary Image (TBI). Euclidean distance is measured and matched between nearest neighbours to find the result. MCYT-SignatureOff-75 database was used. Based on our experiment, the lowest FRR achieved is 6.67% and lowest FAR is 12.44% with only 1.12 second computational time from nearest neighbour classifier. The results are compared with Artificial Neural Network (ANN) classifier.
Automatic signature verification with chain code using weighted distance and ...eSAT Journals
Abstract The signature forgery can be restricted by either online or offline signature verification techniques. It verifies the signature by
performing a match with the pre-processed signature dynamically by detecting the motion of stylus during signature while on
other hand, offline verifies by performing a match using the two dimensional scanned image of the signature. This paper studies
about the various techniques available in offline signature verification along with their shadows.
Keywords: Signature Verification, Weighted Distance, High Pressure Factor, Normalization, Threshold Value
Handwritten Character Recognition: A Comprehensive Review on Geometrical Anal...iosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Offline handwritten signature identification using adaptive window positionin...sipij
The paper presents to address this challenge, we have proposed the use of Adaptive Window Positioning
technique which focuses on not just the meaning of the handwritten signature but also on the individuality
of the writer. This innovative technique divides the handwritten signature into 13 small windows of size nxn
(13x13). This size should be large enough to contain ample information about the style of the author and
small enough to ensure a good identification performance. The process was tested with a GPDS dataset
containing 4870 signature samples from 90 different writers by comparing the robust features of the test
signature with that of the user’s signature using an appropriate classifier. Experimental results reveal that
adaptive window positioning technique proved to be the efficient and reliable method for accurate
signature feature extraction for the identification of offline handwritten signatures .The contribution of this
technique can be used to detect signatures signed under emotional duress
OFFLINE SIGNATURE VERIFICATION SYSTEM FOR BANK CHEQUES USING ZERNIKE MOMENTS,...ijaia
Handwritten signature is the most accepted and economical means of personnel authentication. It can be
verified using online or offline schemes. This paper proposes a signature verification model by combining
Zernike moments feature with circularity and aspect ratio. Unlike characters, signatures vary each time
because of its behavioural biometric property. Signatures can be identified based on their shape. Moments
are the good translational and scale invariant shape descriptors. The amplitude and the phase of Zernike
moments, circularity and aspect ratio of the signature are the features that are extracted and combined for
the verification purpose and are fed to the Feedforward Backpropagation Neural Network. This Neural
Network classifies the signature into genuine or forged. Experimental results reveal that this methodology
of combining zernike moments along with the two mentioned geometrical properties give higher accuracy
than using them individually. The combination of these feature vector yields a mean accuracy of 95.83%.
When this approach is compared with the literature, it proves to be more effective.
ARABIC ONLINE HANDWRITING RECOGNITION USING NEURAL NETWORKijaia
This article presents the development of an Arabic online handwriting recognition system. To develop our
system, we have chosen the neural network approach. It offers solutions for most of the difficulties linked
to Arabic script recognition. We test the approach with our collected databases. This system shows a good
result and it has a high accuracy (98.50% for characters, 96.90% for words).
Fraud Detection Using Signature RecognitionTejraj Thakor
The signature of person is an important bio metric of a human being which can be used to authenticate human identity. The problem arises when someone decide to imitate our signature and steal our identity.
The Image of human signature is collected by camera of mobile phone which can extract dynamic and spatial information of the signature based on Image processing techniques like Convert to gray scale, Noise Removal, Normalization, Border Elimination and Feature Extraction techniques.
The signature matching is depending on SVM. The SVM classifier is trained with sample images in database obtained from those individuals whose signatures have to be authenticated by the system. In our proposed system SQLite database as a back-end and Android platform as a front-end.
Offline Signature Verification Using Local Radon Transform and Support Vector...CSCJournals
In this paper, we propose a new method for signature verification using local Radon Transform. The proposed method uses Radon Transform locally as feature extractor and Support Vector Machine (SVM) as classifier. The main idea of our method is using Radon Transform locally for line segments detection and feature extraction, against using it globally. The advantages of the proposed method are robustness to noise, size invariance and shift invariance. Having used a dataset of 600 signatures from 20 Persian writers, and another dataset of 924 signatures from 22 English writers, our system achieves good results. The experimental results of our method are compared with two other methods. This comparison shows that our method has good performance for signature identification and verification in different cultures.
An offline signature verification using pixels intensity levelsSalam Shah
Offline signature recognition has great importance in our day to day activities. Researchers are trying to use them as biometric identification in various areas like banks, security systems and for other identification purposes. Fingerprints, iris, thumb impression and face detection based biometrics are successfully used for identification of individuals because of their static nature. However, people’s signatures show variability that makes it difficult to recognize the original signatures correctly and to use them as biometrics. The handwritten signatures have importance in banks for cheque, credit card processing, legal and financial transactions, and the signatures are the main target of fraudulence. To deal with complex signatures, there should be a robust signature verification method in places such as banks that can correctly classify the signatures into genuine or forgery to avoid financial frauds. This paper, presents a pixels intensity level based offline signature verification model for the correct classification of signatures. To achieve the target, three statistical classifiers; Decision Tree (J48), probability based Naïve Bayes (NB tree) and Euclidean distance based k-Nearest Neighbor (IBk), are used.
For comparison of the accuracy rates of offline signatures with online signatures, three classifiers were applied on online signature database and achieved a 99.90% accuracy rate with decision tree (J48), 99.82% with Naïve Bayes Tree and 98.11% with K-Nearest Neighbor (with 10 fold cross validation). The results of offline signatures were 64.97% accuracy rate with decision tree (J48), 76.16% with Naïve Bayes Tree and 91.91% with k-Nearest Neighbor (IBk) (without forgeries). The accuracy rate dropped with the inclusion of forgery signatures as, 55.63% accuracy rate with decision tree (J48), 67.02% with Naïve Bayes Tree and 88.12% (with forgeries).
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Proposed Method for Off-line Signature Recognition and Verification using Neu...Editor IJMTER
Computers have become common and are used in almost every field including financial
transactions, thus providing additional security measures is necessary. According to consumer’s
expectations, these security measures must be cheap, reliable and un-intrusive to the authorized
person. The technique which meets these requirements is handwritten signature verification.
Signature verification technique has advantage over other biometric techniques: including voice, iris,
fingerprint, palm etc. as it is mostly used for daily routine procedures link banking operations,
document analysis, electronic funds transfer, access control and many other. Most importantly, it’s
easy and people are less likely to object it. Proposed technique involves using a new approach that
depends on a neural network which enables the user to recognize whether a signature is original or a
fraud. Scanned images are introduced into the computer, their quality is modified with the help of
image enhancement and noise reduction techniques, specific features are extracted and neural
network is trained, The different stages of the process includes: image pre-processing, feature
extraction and pattern recognition through neural networks.
Text content dependent writer identificationeSAT Journals
Abstract
Text content based personal Identification system is vital in resolving problem of identifying unknown document’s writer using a
set of handwritten samples from alleged known writers. Text written on paper document is usually captured as image by scanner
or camera for computer processing. The most challenging problem encounter in text image processing is extraction of robust
feature vector from a set of inconstant handwritten text images obtained from the same writer at different time. In this work new
feature extraction method is engaged to produce active text features for developing an effective personal identification system.
The feature formed feature vector which is fed as input data into classification algorithm based on Support Vector Machine
(SVM). Experiment was conducted to identify writers of query handwritten texts. Result show satisfactory performance of the
proposed system, it was able to identify writers of query handwritten texts.
Keywords: Handwritten Text, Feature Vector, Identification and Support Vector Machine.
Offline Handwritten Signature Identification and Verification using Multi-Res...CSCJournals
In this paper, we are proposing a new method for offline (static) handwritten signature identification and verification based on Gabor wavelet transform. The whole idea is offering a simple and robust method for extracting features based on Gabor Wavelet which the dependency of the method to the nationality of signer has been reduced to its minimal. After pre-processing stage that contains noise reduction and signature image normalisation by size and rotation, a virtual grid is placed on the signature image. Gabor wavelet coefficients with different frequencies and directions are computed on each points of this grid and then fed into a classifier. The shortest weighted distance has been used as the classifier. The weight that is used as the coefficient for computing the shortest distance is based on the distribution of instances in each of signature classes. As it was pointed out earlier, one of the advantages of this system is its capability of signature identification and verification of different nationalities; thus it has been tested on four signature dataset with different nationalities including Iranian, Turkish, South African and Spanish signatures. Experimental results and the comparison of the proposed system with other systems are consistent with desirable outcomes. Despite the use of the simplest method of classification i.e. the nearest neighbour, the proposed algorithm in comparison with other algorithms has very good capabilities. Comparing the results of our system with the accuracy of human\'s identification and verification, it shows that human identification is more accurate but our proposed system has a lower error rate in verification.
OFFLINE SIGNATURE RECOGNITION VIA CONVOLUTIONAL NEURAL NETWORK AND MULTIPLE C...IJNSA Journal
One of the most important processes used by companies to safeguard the security of information and prevent it from unauthorized access or penetration is the signature process. As businesses and individuals move into the digital age, a computerized system that can discern between genuine and faked signatures is crucial for protecting people's authorization and determining what permissions they have. In this paper, we used Pre-Trained CNN for extracts features from genuine and forged signatures, and three widely used classification algorithms, SVM (Support Vector Machine), NB (Naive Bayes) and KNN (k-nearest neighbors), these algorithms are compared to calculate the run time, classification error, classification loss, and accuracy for test-set consist of signature images (genuine and forgery). Three classifiers have been applied using (UTSig) dataset; where run time, classification error, classification loss and accuracy were calculated for each classifier in the verification phase, the results showed that the SVM and KNN got the best accuracy (76.21), while the SVM got the best run time (0.13) result among other classifiers, therefore the SVM classifier got the best result among the other classifiers in terms of our measures.
Freeman Chain Code (FCC) Representation in Signature Fraud Detection Based On...CSCJournals
This paper presents a signature verification system that used Freeman Chain Code (FCC) as directional feature and data representation. There are 47 features were extracted from the signature images from six global features. Before extracting the features, the raw images were undergoing pre-processing stages which were binarization, noise removal by using media filter, cropping and thinning to produce Thinned Binary Image (TBI). Euclidean distance is measured and matched between nearest neighbours to find the result. MCYT-SignatureOff-75 database was used. Based on our experiment, the lowest FRR achieved is 6.67% and lowest FAR is 12.44% with only 1.12 second computational time from nearest neighbour classifier. The results are compared with Artificial Neural Network (ANN) classifier.
Automatic signature verification with chain code using weighted distance and ...eSAT Journals
Abstract The signature forgery can be restricted by either online or offline signature verification techniques. It verifies the signature by
performing a match with the pre-processed signature dynamically by detecting the motion of stylus during signature while on
other hand, offline verifies by performing a match using the two dimensional scanned image of the signature. This paper studies
about the various techniques available in offline signature verification along with their shadows.
Keywords: Signature Verification, Weighted Distance, High Pressure Factor, Normalization, Threshold Value
Handwritten Character Recognition: A Comprehensive Review on Geometrical Anal...iosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
No sql databases new millennium database for big data, big users, cloud compu...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
A case study on energy savings in air conditioning system by heat recovery us...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Securing the cloud computing systems with matrix vector and multi-key using l...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Moderate quality of voice transmission using 8 bit micro-controller through z...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Smfir technology based transportation system and applicability of mppteSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
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IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
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IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
A Novel Automated Approach for Offline Signature Verification Based on Shape ...Editor IJCATR
The handwritten signature has been the most natural and long lasting authentication scheme in which a person draw some
pattern of lines or writes his name in a different style. The signature recognition and verification are a behavioural biometric and is
very challenging due to the variation that can occur in person’s signature because of age, illness, and emotional state of the person. As
far as the representation of the signature is concerned a classical technique of thinning or skeleton is mostly used. In this paper, we
proposed a new methodology for signature verification that uses structural information and original strokes instead of skeleton or
thinned version to analyse the signature and verify. The approach is based on sketching a fixed size grid over the signatures and getting
2-Dimensional unique templates which are then compared and matched to verify a query signature as genuine or forged. To compute
the similarity score between two signature’s grids, we follow template matching rule and the Signature grid’s cell are mapped and
matched with respect to position. The proposed framework is fast and highly accurate with reduce false acceptance rate and false
A Review on Robust identity verification using signature of a personEditor IJMTER
Signature is behavioural type biometrics characteristics of human. Signature has been a
distinguishing feature for person identification. In these days increasing number of transactions,
especially related to financial and business are being authorized via signatures. Two types of
verification methods are: Offline signature verification and online signature verification. In this paper
we review various components of offline signature reorganization and verification system, feature
extraction techniques and available techniques.
RELATIVE STUDY ON SIGNATURE VERIFICATION AND RECOGNITION SYSTEMAM Publications
Signature verification is amongst the first few biometrics to be used for verification and one of the natural
ways of authenticating a person’s identity. The user introduces into the computer the scanned images of the signature,
then after image enhancement and reduction of noise of the image. Followed by feature extraction and neural network
training images of signature are verified. Yet now thousands of financial and business transactions are being
authorized via signatures. Therefore an automatic signature verification system is needed. This paper represents a brief
review on various approaches based on different datasets, features and training techniques used for verification.
Offline Handwritten Signature Verification using Neural Networkijiert bestjournal
The different biometric techniques have been discussed for ident ification. Such as face reading,fingerprint recognition and retina scanning and these are known as vision based i dentification. There are non vision based identifications such as signature verification and the voice recogni tion. Signature verification plays a vital role in the field of the financial,commercial and for the legal matters. Signature by any person considered as the approval for any work so the signature is the preferred authenticat ion. In this paper signature verification is done by means of image processing,geometric feature extraction and by using neural network technique.
Handwritten Signature Verification System using Sobel Operator and KNN Classi...ijtsrd
Signature is one of the most widely accepted personal attributes for identity verification. Signature verification is a scheme to verify cheque for bank security. So, this system is proposed as the off line handwritten signature verification system for the bank cheque image processing. In any offline signature verification system, feature extraction stage is the most vital and difficult stage. In this system, sobel gradient operator is used to extract signature features. After extracting features, this system performs the verification process by using k nearest neighbor KNN classifier. This system supports the security about the bank processing by verifying user signature from the bank cheque. Soe Moe Myint | Moe Moe Myint | Aye Aye Cho "Handwritten Signature Verification System using Sobel Operator and KNN Classifier" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd27825.pdf Paper URL: https://www.ijtsrd.com/engineering/computer-engineering/27825/handwritten-signature-verification-system-using-sobel-operator-and-knn-classifier/soe-moe-myint
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Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
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Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
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• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
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• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
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Offline signature identification using high intensity variations and cross over points based feature extraction
1. IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
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Volume: 03 Issue: 05 | May-2014, Available @ http://www.ijret.org 170
OFFLINE SIGNATURE IDENTIFICATION USING HIGH INTENSITY
VARIATIONS AND CROSS OVER POINTS BASED FEATURE
EXTRACTION
Ravikumar B Panchal1
, Dhaval R Bhojani2
1
M.E Student, Department of Electronics and Communication Engineering, Darshan Institute of Engineering &
Technology, Rajkot, India
2
Assist. Prof and HOD, Department of Electronics and Communication Engineering, Darshan Institute of Engineering &
Technology, Rajkot, India
Abstract
Signature has its own advantage in person identification. The facts that people usually do not putting text in it; rather they draw a
pattern as their signature. Even today, numbers of transactions are increasing related to banking and businesses are being identified
via signatures. The main difficulty lies in the variations of the geometrical representation of the signature which is closely related to
the identity of human beings. Hence, development methods for genuine signature verification must be needed. When bundles of
documents, e.g. bank cheques, have to be verified in a limited time, the manual verification of account holders’ signatures is often
tedious work. So there is a need of Automatic Signature Verification and Identification systems. For that different logic should be
considered to process such signatures. The present paper is done in the field of offline signature identify by extracting some special
domain features that make a signature difficult to forge. In this paper existing signature verification systems have been thoroughly
studied and a model is designed to develop an offline signature idenfication system. Here off-line signature idenfication system that
depends on high intensity variation based features as well as cross over points based features. Main aim is to take various feature
points of a given signature and compares them with the test signatures feature points by choosing appropriate classifiers.
Keywords: signature identification, database creation, preprocessing, high intensity variations and cross over points
based features
----------------------------------------------------------------------***------------------------------------------------------------------------
1. INTRODUCTION
We all are aware about signing various documents. In our
daily life we are doing lot of signatures either it starts from
bank work or in personal documents. So it is necessary to
determine the genuineness and authentication which require
identification marks using signatures. Most signature
verification system required perfect signature that must be
done on proper fixed angle. This cannot all times possible that
it must be samely aligned. In that situations the proposed
system will reject the signature even though it will done by
genuine person. Though various techniques are available for
verification of bank cheques before Clearing, it creates
unavoidable errors. Signature verification system fall into two
categories according to the grasping of the information: On-
line methodology and Off-line methodology.
On-line methodology includes pen through which signatures
are inserted and which are further scanned by sensors. It also
includes location, velocity of pen, acceleration and pen
pressure, as functions of time. Online systems use this
information captured during acquisition. These dynamic
characteristics are specific to each individual and sufficiently
stable as well as repetitive [1].
Off-line data is a two dimensional image of the signature
which is scanned by various scanners. Off-line signature
process is complex task due to the absence of dynamic
geometry of signatures. Difficulty also comes in the fact that
due to different modern and unconventional writing styles, it
is harder to segment signature strokes. The nature as well as
the different pattern of pen may also affect the nature of the
signature obtained. Sometimes signatures of genuine person
cannot do proper way due to illness, mood, and age relaxation
or emotional behaviour. As a result large intra-personal as
well as interpersonal variations are generating. An intelligent
system has to be designed which should not only be able to
consider these factors but also detect various types of forgeries
within less amount of time. The system should neither be too
sensitive nor too coarse. It should have an acceptable trade-off
between a low false acceptance ratio as well as low false
rejection ratio. The designed system should also find such
kind of feature points that reduces less amount of storage as
well as less amount of computational time [2].
2. IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
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Volume: 03 Issue: 05 | May-2014, Available @ http://www.ijret.org 171
2. TYPES OF FORGERY
The basic types of forgery include [1]:
1. Random Forgery: Random forgery is done by a
person who doesn’t know the shape and structure of the
original signature. Fig.1 (b).
2. Simple Forgery: In this type of forgery the person
concerned has a vague idea of the actual signature, but
is signing without much practice. Fig.1 (d).
3. Skilled Forgery: This type of forgery considers
appropriate knowledge about the original signature
along with sample time for proper practice. Our
proposed scheme eliminates random and simple
forgeries and also reduces skilled forgery to a great
extentFig.1(c).
Fig-1 :( a) original signature,(b)random forgery,(c)skilled
forgery,(d)simple forgery
3. RELATED WORK
A novel feature extraction scheme has been suggested for
offline signature verification [1]. This method used the concept
of feature extraction with help of identifying geometric centre
as well as Euclidean distance of different signatures. The
performance of classifier used here is faster as well better for
feature extraction. Results that are achieved by this method are
better than all existing methods. The process of Threshold
selection is done with help of standard deviation and average.
Another method for off-line signature identification and
verification is proposed based on the description of the
signature envelope and the interior stroke distribution in polar
and Cartesian coordinates [2]. In this paper, a new geometrical
feature for an offline signature verification system (ASV) is
used. The proposed features can be calculated with a fixed-
point microprocessor. Therefore, the features can be extracted
from inside a personal device such as a smart card. The system
is check out with various classifiers like SVM, HMM and
EDC for identifying forgeries.
The Improved Offline Signature Verification Scheme Using
Feature Point Extraction Method [3] is proposed for reducing
FAR compare to different proposed methods. The scheme is
based on selection of 60 feature points from the COG of the
signature and compares them with trained feature points. The
classification of the feature points depends on mean and
variance. A smaller change of a signature results in a large
change in the values of threshold distance from the COG.
Therefore in this algorithm the value of FRR is increased.
The generation of a digital skeleton is often one of the first
processing steps taken by a computer vision system when
attempting to extract features from an object in an image.
Various algorithms have been proposed to produce the
skeleton of a digital binary pattern. The Hilditch thinning
algorithm [4] is widely used as a useful method of
preprocessing in image process is proposed for speeding real-
time process. Hilditch proposed an algorithm to obtain the
skeleton of one object in an image. There are two versions for
this algorithm, one using 4×4 mask and the other one using
3×3 mask. With a 3×3 mask image, the result of process
output can be saved to a memory “table”. The output results of
all different 3×3 masks are saved to this “table” at the
beginning of starting application. When an image will be
processed, the thinning results of every 3×3 masks in the
image can be extracted by the method of “looking for table”.
Thus the thinning result is same but the process speed is high.
A method based on multi-feature and multi-stage verification
is proposed in paper [5] for Chinese signature. This paper
carries out a two-stage verification to make decision. For an
input image, extract its direction features firstly. If it is
justified as forgery, the final output decision is forgery; if it is
justified as genuine, and then extracts the dynamic features, to
carry out the second stage verification, and the decision of the
second stage is taken as the final decision.
There is another way to authenticate genuine signature by
using Cross-validated Graph Matching (OSACGM) algorithm
[6]. In this paper, OSACGM (Offline Signature Authentication
using Cross Validate Graph Matching) algorithm is proposed,
in which they use two concepts viz., Graph matching and
Cross-validation for signature verification. The signature
extraction method is used in pre-processing to obtain high
resolution of signature for smaller normalization box. The
signatures are compared by constructing a bipartite graph from
which a minimum cost complete matching is obtained and the
measure of dissimilarity i.e., the Euclidean distance is
determined.
The idea of finding the location variations of the strokes of
signature geometry for signature verification is proposed and
tested [7]. Two methods are proposed. The first method helps
3. IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
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Volume: 03 Issue: 05 | May-2014, Available @ http://www.ijret.org 172
in determine the positional variation of the projection profiles
of the signature, while the second method helps in finding out
the actual positional variations of individual strokes in the 2-D
signature patterns. In both methods, the statistics on these
variations are computed. Here posional variations are finding
out by applying various signatures as a input. The genuineness
of the input is determined by judging the state of the training
sets. The decision process involves the computation of a
distance measure which takes the positional variations and the
correlation between them into account.
Another method for off-line Persian signature identification
and verification is proposed that is based on Image
Registration, DWT (Discrete Wavelet Transform) and Image
Fusion [8]. Training signatures of each person are registered to
overcome shift and scale problem. To extract features, at first,
DWT is used to access details of signature; then several
registered instances of each person signatures are fused
together to generate reference pattern of person's signatures. In
the classification phase, Euclidean distance between the test
image and each pattern is used in different sub bands.
Experimental results confirmed the effectiveness of the
proposed method.
The method for identifying genuineness of bank cheque
signatures is proposed [9]. It describes how signature is
identifies in cheques using various verification algorithm.
Here proposed algorithm can be used for an effective signature
verification system in the banking industry. The proposed
methodology verifies a cheque by recognizing and analyzing
the major details in a cheque, which includes the account
holder’s signature. The results show the FAR and FRR in the
verification process and the success ratio.
In [11] the author presented new approach for signature region
of interest pre processing. He used new auto cropping
preparation on the basis of the image content, where the
intensity value of pixel is the source of cropping. This
approach provides both the possibility of improving the
performance of security systems based on signature images,
and also the ability to use only the region of interest of the
used image to suit layout design of biometric systems.
4. PROPOSED SYSTEM ARCHITECTURE
In order to design a system, which will detect the forge
signatures by comparing some special features with original
one, the following architecture has been proposed.
Original scanned
signature
Preprocessing
Noise
removing
cropping thinning Normalization
Raw data
High Intensity
based extraction
Loop based
extraction
Feature extraction
Test Signature
Preprocessing
Verify by
classifier
The design process can be categorized into three main parts:
Preprocessing
Features extractions from both genuine and test
signatures and
Compare the extracted features between them.
4.1 Pre Processing
The principal objective of preprocessing is to obtain a
transformed image with enhanced quality. It includes Noise
removal, cropping, Thinning and Normalization.
4.1.1 Noise Removal
Noise removal is required to eliminate the pixels that are not
part of the signature, but contained in the image. When we
scan signature from paper then some unwanted pixels comes
with the scanned image that is not a part of the signature. So
this unwanted part must be removed before feature extraction.
4.1.2 Cropping
Cropping process removing unnecessary white back ground
from the image.so as result it reduces the size of signature.
The resultant signature only incudes the main framework of
the signature.
4.1.3 Thinning
Thinning is a morphological process necessary for the
reduction of data and computational time. To reduce all
objects in an image to lines, without changing the essential
structure of the image, use the bwmorph function. Thinning
works for objects consisting of lines (straight or curved). This
method does not work for object having shapes that encloses a
large area. Thinning is most of the time an intermediate
process, to prepare the object for further analysis. It reduces
the signature to a skeleton of unitary thickness.
4. IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
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Volume: 03 Issue: 05 | May-2014, Available @ http://www.ijret.org 173
4.2 Feature Extraction
Each person’s signature has different style. When someone
tries to copy other’s signatures then they basically try to
maintain the shape. But some important features can make a
signature difficult to be copied. Now this features are analyzed
and are used in this proposed method to differentiate genuine
from forge one. Here we use high intensity variation and cross
over points as a feature extraction.
4.2.1 High Intensity Variation
Person usually does signatures with reference to fix angle.
While doing signature person follow same kind of writing
technique. So as result different intensity is generated the
entire signature. Use of ball point as well as ink pan also
create large different in intensity. This feature can be extracted
easily to compare genuine and test signatures. Figure.2 and
figure.3 represents the examples which includes high intensity
variation points shown by arrow in it.
Fig-2 Fig-3
4.2.2 Cross Over Points
Each user has some monopoly in doing signature. Here each
and every time shape of some special letter is always
remaining constant. So as result it creates same cross over
points. This point is very helpful for identify authors own
signature among all. Figure 4 and figure 5 represents example
of which includes cross over points shown by arrow in it.
Fig-4 Fig-5
5. ALGORITHM FOR PRAPOSED SCHEME
Step 1: Hand written signature is scanned.
Step 2: Signature is preprocessed and converted into binary or
gray scale as per requirement, removing noise from signatures,
thinned signature and finally normalize the signature.
Step 3: thinned signature is used for feature extraction. here
special domain feature as high intensity variation points and
cross over points are extracted from genuine as well as test
signature.
Step 4: These features are compared with the features of
original one, which have already been extracted with help of
appropriate classifier.
6. IMPLEMENTATION AND RESULTS
6.1 Database Creation
During my research work we have taken total 100 signatures
from total 10 faculty members.here each faculty member have
to sign total 10 signature.here 10 signature includes 2-genuine
signature,3-training signature and remaining five signature
represents test signature.the following table represents the
total signature database overview.
Table 1 Database overview
Sr no Name of faculty Notification
No of signatures
TotalGenuine Training Test
1 Kishan K Govani KKG 3 2 5 10
2 Ashish j J Donga DA 3 2 5 10
3 Dhaval Patel DD 3 2 5 10
4 Divyang D Vyas DDV 3 2 5 10
5 Dhaval R Bhojani DRB 3 2 5 10
6 Manoj N Popat MNP 3 2 5 10
7
Mitul R
Khandhedia MRK 3 2 5 10
8 Neha Hirani NH 3 2 5 10
9 Raju J Kadivar RJK 3 2 5 10
10 Nitin Rola NR 3 2 5 10
5. IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
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6.2 Noise Remove
We applied a noise removal technique, which basically based
on the size of pixel. In this algorithm some pixels, which are
not connected with rest of the signature and have less than 8
pixel values are considered as noise and removed using
MATLAB (R2012a). we choose 4 reference signatures. The
output of each signature is shown below figure 6 to figure 8
Fig- 6 (a) original sign_DA Fig-6 (b) Noise marked_DA
Fig-6(c) after Noise removed_DA
Fig- 7 (a) original sign_DD Fig-7 (b) Noise marked_DD
Fig-7(c) after Noise removed_DD
Fig-8 (a) original sign_DDV Fig-8 (b) Noise marked_DDV
Fig-8(c) after Noise removed_DDV
6.3 Thinning
To reduce all objects in an image to lines, without changing
the essential structure of the image, use the thinning algorithm.
Figure 9 to figure 12 shows the resultant thinning output of
reference signatures obtained by database respectively.
Fig-9 Thinning sign_DA
Fig-10 Thinning sign_DD
6. IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
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Volume: 03 Issue: 05 | May-2014, Available @ http://www.ijret.org 175
Fig-11 Thinning sign_DDV
6.4 Feature Extraction
After getting the preprocessed signature it is then come to find
the forgery using feature extraction. Here we find out it with
help of high intensity variation points and cross over loops
from signatures. These features could be difficult to copy for a
fake person. Figure represents high intensity variation points
by blue colored “+” sign and cross over points are represented
by red circled” sign.
6.4.1 High Intensity Variation based Feature
Extraction
Fig-12 high intensity variation _DA
Fig-13 high intensity variation _DP
Fig-14 high intensity variation _DDV
6.4.2 Cross over Points based Feature Extraction
Fig-15 cross over points _DA
Fig-16 cross over points _DP
Fig-17 cross over points _DDV
7. IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
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Volume: 03 Issue: 05 | May-2014, Available @ http://www.ijret.org 176
7. CONCLUSIONS
The proposed signature identification system is been based on
some special features extraction. These features included high
intensity variations and cross over points it uses a compact and
memory efficient storage of feature points, which reduces
memory overhead and results in faster comparisons of the data
to be verified. From intuition, the statistics on the positional
variations of the features or strokes of signature samples
should be useful for verification. Here in this paper we
identify the genuineness of the signatures using such kind of
features. Here verification of these signatures can be done by
choosing appropriate classification methods. Similar to other
real world problems, no single approach may solve the
signature verification problem perfectly, and practical
solutions are often derived by combining different approaches.
This technique can be added with any existing verification
system for better result.
REFERENCES
[1]. Banshidhar Majhi, Y Santhosh Reddy, D Prasanna Babu,
“Novel Features for Off-line Signature Verification”
International Journal of Computers, Communications &
Control, Vol.I, No. 1, pp. 17-24, 2006.
[2]. Migual A. Ferrer, Jesus B. Alonso and Carlos M.
Travieso, "Off-line Geometric Parameters for Automatic
Signature Verification Using Fixed-Point Arithmetic", IEEE
Tran. On Pattern Analysis and Machine Intelligence, vol.27,
no.6, June 2005.
[3]. Debasish Jena, Banshidhar Majhi, Saroj Kumar
Panigrahy, Sanjay Kumar Jena, “Improved Offline Signature
Verification Scheme Using Feature Point Extraction Method”.
[4]. Ming Yin, Seinosuke Narita, “Speedup Method for Real-
Time Thinning Algorithm” DICTA2002: Digital Image
Computing Techniques and Applications, Melbourne,
Australia, 21--22 January 2002.
[5]. Yingna Deng, Hong Zhu, Shu Li, and Tao Wang,
“Signature Verification Method Based on the Combination of
Shape and Dynamic Feature”, Department of Automation and
Information Engineering, Xi’an University of Technology,
710048 Xi’an China, 2005.
[6]. Ramachandra, A. C. Pavithra, K. and Yashasvini, K. and
Raja, K. B. and Venugopal, K. R. and Patnaik, L. M., “Cross-
validation for graph matching based Offline Signature
Verification”, In: INDICON 2008, India, pp: 17-22,2008.
[7]. Fang, B., et al, “Off-line signature verification by the
tracking of feature and stroke positions”, Pattern Recognition,
Vol. 36, pp. 91-101, 2003.
[8]. Samaneh Ghandali, Mohsen Ebrahimi Moghaddam, “Off-
Line Persian Signature Identification and Verification Based
on Image Registration and Fusion”, Journal of Multimedia,
vol. 4, no. 3, June 2009.
[9]. M.Jasmin Pemeena, Priya darsini ,K.Murugesan,
Srinivasa Rao Inbathini, A.Jabeena, and K.Sai Tej “Bank
Cheque Authentication using Signature” ,International Journal
of Advanced Research in Computer Science and Software
Engineering , Volume 3, Issue 5, May 2013.
[10]. Raman Maini & Himanshu Aggarwal, “Study and
Comparison of Various Image Edge Detection Techniques”,
International Journal of Image Processing (IJIP), Volume 3,
Issue 1, 2010.
[11]. Bassam Al-Mahadeen, Mokhled S. AlTarawneh and
Islam H. AlTarawneh “Signature Region of Interest using
Auto cropping” IJCSI International Journal of Computer
Science Issues, Vol. 7, Issue 2, No 4, March 2010.
[12]. Guangyu Zhu, Yefeng Zheng,David Doermann, Stefan
Jaeger, “Signature Detection and Matching for Document
Image Retrieval”, IEEE TRANSACTIONS ON PATTERN
ANALYSIS AND MACHINE INTELLIGENCE, VOL. 31,
NO. 11, NOVEMBER 2009.
[13]. Ravi J, Sundernag Hosamani and K B Raja “Off-line
Signature Identification Based on DWT and Spatial Domain
Features” IEEE-20180